Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Machine Learning Engineer, AI Foundations

Waymo
Oxford
3 days ago
Create job alert

Overview

Waymo is an autonomous driving technology company with the mission to be the world\'s most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World\'s Most Experienced Driver—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo\'s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.

The mission of the Waymo AI Foundations team is to develop machine learning solutions addressing open problems in autonomous driving, towards the goal of safely operating Waymo vehicles in dozens of cities and under all driving conditions. AI Foundations areas that we are currently focusing on include reinforcement learning, learning from demonstration, generative modeling, Bayesian inference, hierarchical learning, and robust evaluation.

This role can be based out of the Oxford or London, UK office and the work modality is hybrid. In this role you will report to a Sr. Staff Machine Learning Engineer.

Responsibilities

  • Work with a creative team of people who help to design, train, and evaluate the large-scale ML models that are used throughout Waymo’s systems, both onboard autonomous vehicles and offboard in simulation
  • Frame open-ended real-world problems as well-defined ML problems; develop and apply cutting-edge ML approaches (deep learning, reinforcement learning, imitation learning, etc) to these problems; scale them to Google-sized data pipelines; and streamline them to run in real-time on the cars
  • Collaborate with other teams including the ML infrastructure, data science and systems engineering teams, as well as various research teams such as Waymo Research, Google Brain, DeepMind and academia

Qualifications

  • Good programming skills - Python, JAX, TensorFlow/PyTorch
  • Strong statistical / ML knowledge

Preferred qualifications

  • C++ experience
  • ML infra experience: training and evaluating ML models at scale
  • Deep learning experience, especially with generative models, e.g., LLMs/VLMs, and/or reinforcement learning or imitation learning
  • Autonomous driving experience

Compensation and details

  • Salary range: £72,000—£78,000 GBP
  • Seniority level: Entry level
  • Employment type: Full-time
  • Job function: Engineering and Information Technology
  • Industries: Technology, Information and Internet


#J-18808-Ljbffr

Related Jobs

View all jobs

Machine Learning Engineer, AI Foundations

Machine Learning Engineer (LLM)

Machine Learning Engineer - 2/3 Month Contract

Senior Machine Learning | AI Engineer

Machine Learning Engineer - 2/3 Month Contract

Machine Learning Engineer

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.

Machine Learning Team Structures Explained: Who Does What in a Modern Machine Learning Department

Machine learning is now central to many advanced data-driven products and services across the UK. Whether you work in finance, healthcare, retail, autonomous vehicles, recommendation systems, robotics, or consumer applications, there’s a need for dedicated machine learning teams that can deliver models into production, maintain them, keep them secure, efficient, fair, and aligned with business objectives. If you’re hiring for or applying to ML roles via MachineLearningJobs.co.uk, this article will help you understand what roles are typically present in a mature machine learning department, how they collaborate through project lifecycles, what skills and qualifications UK employers look for, what the career paths and salaries are, current trends and challenges, and how to build an effective ML team.

Why the UK Could Be the World’s Next Machine Learning Jobs Hub

Machine learning (ML) is becoming essential to industries across the globe—from finance and healthcare to retail, logistics, defence, and the public sector. Its ability to uncover patterns in data, make predictions, drive automation, and increase operational efficiency has made it one of the most in-demand skill sets in the technology world. In the UK, machine learning roles—from engineers to researchers, product managers to analysts—are increasingly central to innovation. Universities are expanding ML programmes, enterprises are scaling ML deployments, and startups are offering applied ML solutions. All signs point toward a surging need for professionals skilled in modelling, algorithms, data pipelines, and AI systems. This article explores why the United Kingdom is exceptionally well positioned to become a global machine learning jobs hub. It examines the current landscape, strengths, career paths, sector-specific demand, challenges, and what must happen for this vision to become reality.